While GANformer2 is an unconditional model, it shares some high-level ideas with conditional text-to-image [1]}, [2]} and scene-to-image [3]}, [4]} techniques, which use semantic layouts as a bottleneck intermediate representation. But whereas these works focus on heavily-engineered pipelines consisting of multiple pre- and post-processing stages and different sources of supervision, including textual descriptions, scene graphs, bounding boxes, segmentation masks and images, we propose a simpler, more elegant design, with a streamlined architecture and fewer supervision signals – relying on images and auto-predicted layouts only, to widen its applicability across domains. Finally, our approach relates to prior works on visual editing [5]}, [6]}, [7]}, [8]} and image compositing [9]}, [10]}, [11]}, [12]}, but while these methods modify an existing source image, GANformer2 stands out being capable of creating structured manipulatable scenes from scratch.
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